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R E V I E W Open Access
Ten years of research in spectrum sensing andsharing in cognitive radioLu Lu*, Xiangwei Zhou, Uzoma Onunkwo and Geoffrey Ye Li
Abstract
Cognitive radio (CR) can successfully deal with the growing demand and scarcity of the wireless spectrum. To
exploit limited spectrum efficiently, CR technology allows unlicensed users to access licensed spectrum bands.
Since licensed users have priorities to use the bands, the unlicensed users need to continuously monitor the
licensed users activities to avoid interference and collisions. How to obtain reliable results of the licensed users
activities is the main task for spectrum sensing. Based on the sensing results, the unlicensed users should adapt
their transmit powers and access strategies to protect the licensed communications. The requirement naturallypresents challenges to the implementation of CR. In this article, we provide an overview of recent research
achievements of including spectrum sensing, sharing techniques and the applications of CR systems.
Keywords: cognitive radio, cooperative communications, spectrum sensing, spectrum sharing.
1. IntroductionDue to the rapid growth of wireless communications,
more and more spectrum resources are needed. Within
the current spectrum framework, most of the spectrum
bands are exclusively allocated to specific licensed ser-
vices. However, a lot of license d band s, such as thos e
for TV broadcasting, are underutilized, resulting in spec-trum wastage [1]. This has promoted Federal Communi-
cations Commission (FCC) to open the licensed bands
to unlicensed users through the use of cognitive radio
(CR) technology [2-6]. The IEEE 802.22 working group
[7] has been formed to develop the air interference for
opportunistic secondary access to TV bands.
In practice, the unlicensed users, also called secondary
users (SUs), need to continuously monitor the activities
of the licensed users, also called primary users (PUs), to
find the spectrum holes (SHs), which is defined as the
spectrum bands that can be used by the SUs without
interfering with the PUs. This procedure is called spec-trum sensing [8-10]. There are two types of SHs, namely
temporal and spatial SHs [9], respectively. A temporal
SH appears when there is no PU transmission during a
certain time period and the SUs can use the spectrum
for transmission. A spatial SH appears when the PU
transmission is within an area and the SUs can use the
spectrum outside that area.
To determine the presence or absence of the PU
transmission, different spectrum sensing techniques
have been used, such as matched filtering detection,
energy detection, and feature detection [11]. However,
the performance of spectrum sensing is limited by noiseuncertainty, multipath fading, and shadowing, which are
the fundamental characteristics of wireless channels. To
address this problem, cooperative spectrum sensing
(CSS) has been proposed [12] by allowing the collabora-
tion of SUs to make decisions.
Based on the sensing results, SUs can obtain informa-
tion about the channels that they can access. However,
the channel conditions may change rapidly and the
behavior of the PUs might change as well. To use the
spectrum bands effectively after they are found available,
spectrum sharing and allocation techniques are impor-
tant [6,13]. As PUs have priorities to use the spectrumwhen SUs co-exist with them, the interference generated
by the SU transmission needs to be below a tolerable
threshold of the PU system [14]. Thus, to manage the
interference to the PU system and the mutual interfer-
ence among SUs, power control schemes should be
carefully designed. By utilizing advanced technologies
such as multiple-input multiple-output (MIMO) and
beamforming with smart antenna, interference-free* Correspondence: [email protected]
School of Electrical and Computer Engineering, Georgia Institute of
Technology, Atlanta, GA 30332-0250, USA
Lu et al. EURASIP Journal on Wireless Communications and Networking 2012, 2012:28
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2012 Lu et al; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons AttributionLicense (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work is properly cited.
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co-exiting transmission can be achieved [15]. In the
multi-hop CR system, relays can assist SUs transmis-
sion, which generate spatial SHs and help to achieve
more communication opportunities. Moreover, the
resource competition among SUs needs to be addressed.
There are a lot of progresses on CR technology in the
last ten years. This article provides an overview of some
recent techniques, potential challenges, and future appli-
cations of CR. In Section 2, fundamental spectrum sen-
sing techniques are provided. In Section 3, CSS
techniques to boost the sensing performance are pre-
sented. Spectrum sharing and allocation schemes are
discussed in Section 4. The applications of CR technol-
o gy a nd c on cl us io ns a re i n S ec ti on s 5 a nd 6 ,
respectively.
Table 1 lists some abbreviations that have been or will
be used in this article.
2. Local spectrum sensingSpectrum sensing enables SUs to identify the SHs,
which is a critical element in CR design [9,10,16]. Figure
1 shows the principle of spectrum sensing. In the figure,
the PU transmitter is sending data to the PU receiver in
a licensed spectrum band while a pair of SUs intends to
access the spectrum. To protect the PU transmission,
the SU transmitter needs to perform spectrum sensing
to detect whether there is a PU receiver in the coverage
of the SU transmitter.
Instead of detecting PU receiver directly, the SU trans-
mitter can detect the presence or absence of PU signals
easily. However, as shown in Figure 1, the radius of PU
transmitter and PU receiver detections are different,
which lead to some shortcomings and challenges. It may
happen that the PU receiver is outside the PU transmit-
ter detection radius, where the SH may be missed. Since
the PU receiver detection is difficult, most study focuses
on PU transmitter detection [6,13].
It is worth noting that, in general, it is difficult for the
SUs to differentiate the PU signals from other pre-exist-
ing SU transmitter signals. Therefore, we treat them all
as one received signal, s(t). The received signal at the
SU, x(t), can be expressed as [17]
x(t) =
n(t) H0,
s(t) + n(t)H1,(1)
where n(t) is the additive white Gaussian noise
(AWGN). H0 and H1 denote the hypotheses of the
absence and presence of the PU signals, respectively.
The objective for spectrum sensing is to decide between
H0 and H1 based on the observation x(t).
Table 1 List of abbreviations
AF Amplify-and-forward
AWGN Additive white Gaussian noise
BS Base station
CAF Cyclic autoco rrelat ion function
CR Cognitive radio
CSD Cyclic spectrum density
CSS Cooperative spectrum sen sing
DoD Department of defense
DR Detect-and-relay
ECC Electronic Communications Committee
FCC Federal Communications Commission
GLRT Generalized likelihood ratio test
HDD Hard-decision-based detection
IPC Interference powers constraint
LRT Likelihood ratio test
LTE-A LTE-Advanced
MIMO Multiple-input multiple-output
MLE Maximum likelihood estimate
MME Maximum-minimum eigenvalue
MMSE Minimum mean-square-error
NE Nash equilibrium
NP Neyman-Pearson
Ofcom Off ice of Communications
OFDMA Orthogonal frequency division multiple access
OP C Outage probability const raint
OSA Opport unistic spectrum access
OSO Opportunistic spatial orthogonalization
PDF Probability density funct ion
PSD Power spectral density
PU Primary user
QAM Quadrature amplitude modulation
QoS Quality of service
QCQP Quadratically constrained quadratic problem
RF Radio-front
RLC Rate loss constraint
ROC Receiver operating character istics
SBS Secondary base station
SDD Soft-decision-based detection
SDR Semidefinite relaxation
SFD Spectral feature detector
SH Spectrum holes
SINR Signal-to-interference-plus-noise ratio
SNR Signal-to-noise ratio
SPRT Sequential probabi li ty ratio test
SU Secondary user
TPA Transmit power allocation
TPC Transmit power constraint
XG Next generation
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The detection performance is characterized by the
probabilities of detection, Pd, and false-alarm, Pf. Pd is
the probability that the decision is H1 , while H1 is
true; Pfdenotes the probability that the decision is H1 ,
while H0 is true. Based on Pd, the probability of miss-
detection Pm can be obtained byPm = 1 - Pd.
2.1. Hypothesis testing criteria
There are two basic hypothesis testing criteria in spec-
trum sensing: the Neyman-Pearson (NP) and Bayes
tests. The NP test aims at maximizing Pd (or minimizing
Pm) under the constraint ofPf a, where a is the maxi-
mum false alarm probability. The Bayes test minimizes
the average cost given by
R =1
i=0
1j=0 Cij Pr(Hi|Hj)Pr(Hj) , where Cij are the
cost of declaring Hi when Hj is true, Pr(Hi) is the
prior probability of hypothesis Hi
and Pr(Hi|Hj) is the
probability of declaring Hi when Hj is true. Both of
them are equivalent to the likelihood ratio test (LRT)
[18] given by
(x) =P(x|H1)
P(x|H0)=P(x(1),x(2), . . . ,x(M)|H1)
P(x(1),x(2), . . . ,x(M)|H0)
H1
H0
,(2)
where P(x(1),x(2), . . . ,x(M)|Hi) is the distribution of
observations x = [x(1), x(2), ..., x(M)]T under hypothesis
Hi , i {0, 1}, (x) is the likelihood ratio, M is the
number of samples, and g is the detection threshold,
which is determined by the maximum false alarm prob-
ability, a, in NP test and = Pr(H0)(C10 C00)
Pr(H1)(C01 C11)in the
Bayes test.
In both tests, the distributions of P(x|Hi) , i {0, 1},
are known. When there are unknown parameters in the
probability density functions (PDFs), the test is called
composite hypothesis testing. Generalized likelihood
ratio test (GLRT) is one kind of the composite hypoth-
esis test. In the GLRT, the unknown parameters are
determined by the maximum likelihood estimates (MLE)
[19-21]. GLRT detectors have been proposed for multi-
antenna systems in [19] and for sensing OFDM signals
in [20,21] by taking some of the system parameters,
such as channel gains, noise variance, and PU signal var-
iance as the unknown parameters.
Sequential testing is another type of hypothesis test-
ing, which requires a variable number of samples to
make decisions. The sequential probability ratio test
(SPRT) minimizes the sensing time subject to the detec-
tion performance constraints [22]. In the SPRT, samples
are taken sequentially and the test statistics are com-
pared with two threshold g0 and g1 (g0 g1; H0 if(x)
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The decision is made by comparing Y with a thresh-
old, g. IfY g, the SU makes a decision that the PU sig-
nal is present (H1 ); otherwise, it declares that the PU
signal is absent (H0 ).
The energy detector is easy to implement and requires
no prior information about the PU signal. However, the
uncertainty of noise power imposes fundamental limita-
tions on the performance of the energy detector [24-26].
Below an SNR threshold, a reliable detection cannot be
achieved by increasing the sensing duration. This SNR
threshold for the detector is called SNR wall [24]. With
the help of the PU signal information, the SNR wall can
be mitigated, but it cannot be eliminated [25]. More-
over, the energy detector cannot distinguish the PU sig-
nal from the noise and other interference signals, which
may lead to a high false-alarm probability.
2.2.3. Feature detector
Cyclostationary detector is one of the feature detectorsthat utilize the cyclostationary feature of the signals for
spectrum sensing [27,28]. It can be realized by analyzing
the cyclic autocorrelation function (CAF) of the received
signal x(t), expressed as
R()x () = E[x(t)x
(t )ej2 t], (4)
where E[] is the expectation operation, * denotes
complex conjugation, and b is the cyclic frequency. CAF
can also be represented by its Fourier series expansion,
called cyclic spectrum density (CSD) function [29],
denoted as
S(f,) =
+=
R()x ()e
j2f. (5)
The CSD function exhibits peaks when the cyclic fre-
quency, b, equals the fundamental frequencies of the
transmitted signal. Under hypothesis H0 , the CSD func-
tion does not have any peaks since the noise is, in gen-
eral, non-cyclostationary.
Generally, feature detector can distinguish noise from
the PU signals and can be used for detecting weak sig-
nals at a very low SNR region, where the energy detec-
tion and matched filtering detection are not applicable.In [30], a spectral feature detector (SFD) has been pro-
posed to detect low SNR television broadcasting signals.
The basic strategy of the SFD is to correlate the period-
ogram of the received signal with the selected spectral
features of a particular transmission scheme. The pro-
posed SFD is asymptotically optimal according to the
NP test, but with lower computational complexity.
To capture the advantages of the energy detector and
the cyclostationary detector while avoiding the disadvan-
tages of them, a hybrid architecture, associating both of
them, for spectrum sensing has been proposed in [ 31].
It consists of two stages: an energy detection stage that
reflects the uncertainty of the noise and a cyclostation-
ary detection stage that works when the energy detec-
tion fails. The proposed hybrid architecture can detect
the signal efficiently.
2.2.4. Other techniques
There are several other spectrum sensing techniques,
such as eigenvalue-based and moment-based detectors.
In a multiple-antenna system, eigenvalue-based detec-
tion can be used for spectrum sensing [32,33]. In [32],
maximum-minimum eigenvalue and energy with mini-
mum eigenvalue detectors have been proposed, which
can simultaneously achieve both high probability of
detection and low probability of false-alarm without
requiring information of the PU signals and noise power.
In most of the existing eigenvalue-based methods, the
expression for the decision threshold and the probabil-
ities of detection and false-alarm are calculated based onthe asymptotical distributions of eigenvalues. To address
this issue, the exact decision threshold for the probability
of false-alarm for the MME detector with finite numbers
of cooperative SUs and samples has been derived in [33],
which will be discussed in Section 3.
When accurate noise variance and PU signal power
are unknown, blind moment-based spectrum sensing
algorithms can be applied [34]. Unknown parameters
are first estimated by exploiting the constellation of the
PU signal. When the SU does not know the PU signal
constellation, a robust approach that approximates a
finite quadrature amplitude modulation (QAM) constel-
lation by a continuous uniform distribution has been
developed [34].
2.3. Sensing scheduling
When and how to sense the channel are also crucial for
spectrum sensing. Usually, short quiet periods are
arranged inside frames to perform a coarse intra-frame
sensing as a pre-stage for fine inter-frame sensing [35].
Accordingly, intra-frame sensing is performed when the
SU system is quiet and its performance depends on the
sample size in the quiet periods. The frame structure for
CR network is shown in Figure 2. Based on this struc-ture, there are sensing-transmission tradeoff problems.
Under the constraint of PU system protection, the opti-
mal sensing time to maximize the throughput [36] and
to minimize outage probability [37] of the SU system
have been studied, respectively.
However, there are some problems about the conven-
tional structure: (1) the sample size of the quiet periods
may not be enough to get good sensing performance;
(2) all CR communications have to be postponed during
channel sensing; (3) the placement of the quiet periods
causes an additional burden of synchronization. To
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address these problems, novel spectrum sensing sche-
duling schemes have been proposed.
In [38], adaptively scheduling spectrum sensing and
transmitting data schemes have been proposed to mini-
mize the negative effect caused by the traditional struc-
ture. The spectrum sensing is carried out when the
channels are in poor conditions and data are trans-
mitted when the channels are good. In [39], sensing per-
iod has been optimized to make full use of opportunities
in the licensed bands. Moreover, a channel-sequencing
algorithm has been proposed to reduce the delay in
searching for an idle channel. To increase the samplesize, quiet-active and active sensing schemes have been
proposed [40]. In the quiet-active sensing scheme, the
inactive SUs sense the channel in both the quiet and
active data transmission periods. To fully avoid synchro-
nization of quiet-periods, pure active sensing has been
proposed where the quiet periods are replaced by quiet
samples in other domains, such as quiet sub-carriers in
orthogonal frequency division multiple access (OFDMA)
systems.
2.4. Challenges
2.4.1. Wideband sensing
Wideband sensing faces technical challenges and thereis limited work on it. The main challenge stems from
the high data rate radio-front (RF) end requirement to
sense the whole band, with the additional constraint
that deployed CR systems (like mobile phones) will be
limited in data processing rates. To achieve reliable
results, the sample rate should be above the Nyquist
rate if conventional estimation methods are used, which
is a challenging task. Alternatively, the RF end can use a
sequence of narrowband bandpass filters to turn a wide-
band signal into narrow-band ones and sense each of
them [41]. However, a large number of RF components
are needed for the whole band. For more effective SU
networks, a multiband sensing-time-adaptive joint detec-
tion framework has been proposed in [42,43], which
adaptively senses multiple narrowband channels jointly
to maximize the achievable opportunistic throughput of
the SU network while keeping the interference with the
PU network bounded to a reasonably low level. Based
on energy detector for narrowband sensing, the sensing
time and detection thresholds for each narrowband
detector are optimized jointly, which is different from
the previous multiband joint detection framework in[44].
2.4.2. Synchronization
Besides the synchronization issue for quiet sensing per-
iod, spectrum synchronization before the data transmis-
sion for non-contiguous OFDM based systems is also a
challenge. To address this challenge [45], received train-
ing symbols can be used to calculate a posterior prob-
ability of each subbands being active without the
information of out-of-band spectrum synchronization.
The proposed hard-decision-based detection (HDD) uti-
lizes a set of adjacent subbands while the soft-decision-
based detection (SDD) uses all the subbands for detec-
tion. Both HDD and SDD schemes provide satisfactoryperformance while the SDD performs better.
3. Cooperative spectrum sensingThe performance of spectrum sensing is limited by
noise uncertainty, multipath fading, and shadowing,
which are the fundamental characteristics of wireless
channels. If the PU signal experiences deep fading or
blocked by obstacles, the power of the received PU sig-
nal at the SU may be too weak to be detected, such as
the case for SU3 as shown in Figure 3. I f t h e S U
W T W
Frame 1 Frame 2 Frame k...
sensing transmission / silent
Figure 2 Frame structure for periodic spectrum sensing, where denotes the sensing period and T - denotes the data transmission
or silent period.
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transmitter cannot detect the presence of the PU trans-
mitter while the PU receiver is within the transmission
range of the SU, the transmission of the PU will be
interfered. To address this problem, CSS has been pro-posed [12]. With the collaboration of several SUs for
spectrum sensing, the detection performance will be
improved by taking advantage of independent fading
channels and multiuser diversity. Based on the decision
fusion criteria, CSS can be realized in either a centra-
lized or a distributed manner.
3.1. Centralized CSS
A centralized CSS system consists of a secondary base
station (SBS) and a number of SUs. In this system, the
SUs first send back the sensing information to the SBS.
After that, the SBS will make a decision on the presence
or absence of the PU signal based on its received infor-mation and informs the SUs about the decision.
3.1.1. Data fusion schemes
Different data fusion schemes for CSS have been stu-
died. Reporting data from the SUs may be of different
forms, types, and sizes. In general, the sensing informa-
tion combination at the SBS can be categorized as soft
combination and hard combination techniques.
Soft Combination In soft combination, the SUs can
send their original or processed sensing data to the SBS
[4]. To reduce the feedback overhead and computational
complexity, various soft combination schemes based on
energy detection have been investigated [46]. In theseschemes, each SU sends its quantized observed energy
of the received signal to the SBS. By utilizing LRT at the
SBS, the obtained optimal soft combination decision is
based on a weighted summation of those energies.
At the SBS, linear combination of the test statistics
from the SUs is the most common fusion rule. The glo-
bal test statistic of linear combination is
Yc =
Nj=1
wjYj, (6)
where Yj is the local test statistics (e.g. received energy
in energy detection or in matched filtering [47,48]) from
SUj, and wj is the weight. Yc is compared with a thresh-
old, gc, to make decisions. The optimization of linear
CSS for the general model is non-convex and challen-
ging. According to a taxonomy based on the probabil-
ities of false alarm and detection, three kinds of CR
systems have been developed, namely conservative,
aggressive, and hostile [47]. Since the last kind is too
complex and of limited interest in applications, only the
first two have been studied in [47]. Recently, a general
model for all the modes has been investigated in [48].
The problem of determining the weights to maximize
the detection probability under a given targeted false-
alarm probability has been studied. Based on the solu-
tion of a polynomial equation, the global optimum is
found by an explicit algorithm.
The linear CSS design does not only focus on thedetection probability optimization but also the tradeoff
in sensing time setting. In a multi-channel system based
on linear CSS, the optimal value of the decision thresh-
old, gc, and sensing time, , are obtained by maximizing
the throughput of the SU system for a given detection
probability [49]. The original non-convex problems can
be successfully converted into convex subproblems. To
avoid the convex approximation, an alternative optimi-
zation technique based on genetic algorithms [50] has
been proposed to directly search for the optimal
solution.
Although soft combination schemes can provide good
detection performance, the overhead for feedback infor-
mation is high. It makes the CSS impractical under a
large number of cooperative SUs. A soften-hard combi-
nation with two-bit overhead [46] has been proposed to
provide comparable performance with less complexity
and overhead.
Hard combination For hard combination, the SUs feed
back their own binary decision results to the SBS. Let uidenotes the local decision of SUi, where ui = 1 and 0
indicate the presence (H1 ) and the absence (H0 ) of the
PU signal, respectively. u denotes the decision of the
SBS. The most common fusion rules are OR-rule, AND-
rule, and majority rule. Under the OR-rule, u = 1 if there exists ui = 1. The AND-rule refers to the SBS
determines u = 1 if ui = 1, for all i. For the majority
rule, if more than half of the SUs report ui = 1, the SBS
decides u = 1. These fusion rules can be generalized to
a K-out-of-N rule, where u = 1 ifKout ofN SUs report
the presence of the PU signal. When K= 1 and K= N,
the K-out-of-N rule becomes the OR-rule and the
AND-rule, respectively.
When the OR-rule or the AND-rule is used, the
threshold of detector should be adjusted according to N
SU1
SU2
SU3
SBS
PU
transmitter
PU
receiver
Figure 3 Cooperative spectrum sensing model, where SU1 is
shadowed over the reporting channel to the secondary base
station (SBS) and SU3 is shadowed over the sensing channel.
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to get better performance than a non-cooperative sys-
tem [51]. For the K-out-of-N rule, the optimal value of
K and sensing time are obtained in [52] by maximizing
the average achievable throughput of the SU system
subject to a detection performance requirement. When
all the SUs employ identical constant detection thresh-
old, an optimal K has been derived to improve both
false-alarm and miss-detection probabilities in [53].
When the SUs have different detection SNRs, it is not
efficient to use the K-out-of-N fusion rule since it
ignores the difference between decisions from a SU with
high detection SNR and a SU with low detection SNR.
Weighted decision fusion schemes have been proposed
to take into account the difference in the reliability of
the decisions made by different SUs [54], which are
reflected in the weights of the decisions at the SBS. The
optimal fusion rules in three different scenarios have
been derived during the optimization of the sensing-throughput tradeoff problem. To ensure reliable detec-
tion, the correlation among different SUs should also be
taken into consideration. A linear-quadratic fusion strat-
egy has been proposed in [55] to exploit the correlation,
which significantly enhances the detection performance.
3.1.2. User selection
User selection in CSS is crucial. Since SUs are located
differently and strengths of received PU signals are dif-
ferent, it is shown in [51] that cooperation of all the
SUs is not optimal. The optimal detection/false-alarm
probabilities are achieved by selectively cooperating
among SUs with high detection SNRs of the PU signal.
The user selection is hard for the detection of small-
scale PU signals that have small-footprint due to their
weak power and unpredictability of spatial and temporal
spectrum usage patterns [56]. Data-fusion range is iden-
tified as a key factor that enables effective CSS. The SUs
in the data-fusion range cooperate to sense PU signals
while others do not [56].
In multi-channel CR networks, it is impractical to
make SUs to sense all the channels. The multi-channel
coordination issues, such as, how to assign SUs to sense
channels and to maximize the expected transmission
time, have been studied in [57]. It has been shown that
multi-channel coordination can improve CSS perfor-mance. Similar issues can be also found in sensor net-
works [58].
If the SUs cannot distinguish the signals from the PU
and other SUs, it may lose the opportunity to access the
spectrum [59]. The presence/absence of possible inter-
ference from other SU transmitters is a major compo-
nent o f the uncertainty l imit ing the detection
performance. Coordinating the nearby SUs can reduce
the uncertainty [59].
3.1.3. Sequential CSS
In CSS, SPRT can opportunistically reduce the sample
size required to meet the reliability target. In [60],
sequential detection scheme has been designed to mini-
mize the detection time. In the scheme, each SU calcu-
lates the log-likelihood ratio of its measurement and theSBS accumulates these statistics to determine whether
or not to stop making measurements. A robust design is
developed for the scenarios with unknown system para-
meters, such as noise variance and signal power. More-
over, a tradeoff between sensing time and average data
rate of the SUs based on sequential sensing for multi-
channel system has been studied in [61]. A stopping
policy and an access policy are given to maximize the
total achievable rate of the SU system under a mis-
detection probability constraint for each channel.
3.1.4. Compressive sensing
Compressive sensing can be applied as an alternative toreduce the sensing and feedback overhead. In [62], each
SU senses linear combinations of multiple narrow bands
by selective filters. The results are reported to the SBS,
where matrix completion and joint sparsity recovery
algorithms are applied to decode the occupied channels.
Both algorithms allow exact recovery from incomplete
reports and reduce feedback from the SUs to the SBS.
Compressive sensing can also be used with other techni-
ques [63].
3.2. Distributed CSS
In the centralized CSS, the cooperative SUs need to feed
back information to the SBS, which may incur high
communication overhead and make the whole network
vulnerable to node failure. To address these problems,
distributed CSS can be applied.
In the CR networks, an SU can act as a relay for
others to improve sensing performance [64-66]. For the
scheme in [64], one SU works as a amplify-and-forward
(AF) relay for another SU to get the agility gain when
the relay user detects the high PU signal power and the
link between two SUs is good. The scheme is extended
into multi-user networks [65]. To ensure asymptotic agi-
lity gain with probability one, a pairing protocol is
developed. Besides AF relay scheme, a detect-and-relay(DR) scheme has been proposed [66], where only the
relay SUs that detect the present of the PU signals for-
ward the received signals to the SU transmitter. The
results show that DR mode outperforms AF mode.
By using both temporal redundant information in two
adjacent sensing periods and the spatial redundant
information between two adjacent SUs, a space-time
Bayesian compressive CSS for wideband networks has
been developed to combat noise [67]. For the multi-hop
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CR networks, a scheme [68] has been proposed to com-
press the signal in the time domain rather than the
power spectral density (PSD) domain by letting each SU
estimate PU transmitter and its own signal iteratively,
and exchanging information with its neighboring SUs to
get the global decision about the availability of the
spectrum.
3.3. Location awareness
CR networks may be equipped location and environ-
mental awareness features [69] to further improve the
performance. A conceptual framework for the location-
awareness engine has been developed in [70]. Then, a
CR positioning system has been introduced in [71] to
facilitate cognitive location sensing. The location infor-
mation of PUs and SUs can be used for determining
spatial SHs [72]. Moreover, it is very important in public
safety CR systems to detect and locate victims [73]. Theabove is only initial research in the area and more study
is desired in the future.
3.4. Challenges
3.4.1. Common control channel
Common control channel between the SUs and the SBS
is assumed in most of existing work, which requires
extra channel resources and introduces additional com-
plexity. Moreover, in the CR networks, it is difficult to
establish a control channel at the beginning of the sen-
sing stage and the change of the PUs activities may
affect the established control channel. In [74], a selec-
tive-relay based CSS scheme without common reporting
control channels has been proposed. To limit interfer-
ence to the PUs, only the relays (SUs) that detect the
absence of the PU signal feedback to the SBS. The SBS
then uses the received signals that experience fading to
make a decision. Compared with the traditional scheme
with common reporting control, the proposed scheme
does not sacrifice the performance of the receiver oper-
ating characteristics (ROC). How to set up and maintain
common control channel is still a challenge and an
open issue for CR networks.
3.4.2. Synchronization
Most study is based on synchronous local observations.However, SUs locate at different places in practical CR
systems, resulting in a synchronization problem for data
fusion. To enable combination of both synchronous and
asynchronous sensing information from different SUs, a
probability-based combination method has been pro-
posed in [75] by taking the time offsets among local
sensing observations into account.
3.4.3. Non-ideal information
Most of the study analyzes the performance of CSS
based on the perfect knowledge of the average received
SNR of the PU transmitter signal. However, in practice,
this is not always the case. The effect of average SNR
estimation errors on the performance of CSS has been
examined in [76]. In the noiseless-sample-based case,
the probability of false alarm decreases as the average
SNR estimation error decreases for both independent
and correlated shadowings. In the noise-sample-based
case, there exists a surprising threshold for the noise
level. Below the threshold, the probability of false alarm
increases as the noise level increases, where the prob-
ability decreases as the noise increases above the
threshold.
4. Spectrum allocation and sharingIn the previous sections, we have discussed the spec-
trum sensing techniques for CR networks. Based on
the sensing results, the SUs have information about
the channels that they can access. However, the chan-
nel conditions may change rapidly and the behaviorof the PUs might change as well. In order to achieve
better system performance, SUs should decide which
channel can be used for transmission together with
when and how to access the channel. To protect the
PU system, the interference generated by the SUs
should also be taken into account. Moreover, one SU
needs to consider the behavior of other co-existing
SUs. In the section, we will discuss the spectrum allo-
c a ti on a nd s ha ri ng s ch e me s t o a d dr es s t h es e
problems.
Depending on spectrum bands that the SUs use, the
schemes can be divided into two types, namely open
spectrum sharing and licensed spectrum sharing [6,13].
In the open spectrum sharing system, all the users have
the equal right to access the channels. The spectrum
sharing among SUs for the unlicensed bands belongs to
this type. The licensed spectrum sharing can also be
called hierarchical spectrum access model. In such sys-
tems, the licensed PUs have higher priorities than the
unlicensed SUs. Usually, there are no conflicts among
PUs since they all have their own licensed bands. For
the SUs, they need to adjust their parameters, such as
transmit power and transmission strategy, to avoid the
interruption to the PUs. According to the access strate-
gies of the SUs, the hierarchical spectrum access modelcan be further divided into spectrum underlay and spec-
trum overlay [13]. In the spectrum underlay system, the
SUs are allowed to transmit while the PUs are transmit-
ting. The interference generated from the SUs need to
be constrained to protect the PUs. The power control
problem is one of the key issues in the systems. In the
spectrum overlay systems, the SUs can only transmit
when PUs are not or the SUs create interference-free
transmission to the PUs by using some advanced techni-
ques. Spectrum overlay is also called opportunistic spec-
trum access (OSA).
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Another classification depends on whether there exists
a central node to manage spectrum allocation and
access procedure [6]. The whole procedure may be con-
trolled by a central node. Due to the cost of the central
node and information feedback, the centralized
approaches may be impractical in some cases. In this
case, the SUs may make their own decisions based on
the observations of the local spectrum dynamics. This is
called distributed spectrum sharing. Of course, several
SUs in a system may cooperate with each other, which
is called cooperative spectrum sharing [6].
In the following, we will discuss some important tech-
niques on spectrum allocation and sharing.
4.1. Resource allocation and power control
In order to limit interference to the PUs created by the
SUs, various resource allocation and power control
schemes have been proposed for the CR networks.4.1.1. Single-carrier and single-antenna systems
For a point-to-point system with single antenna, the
spectrum sharing model can be shown as in Figure 4,
where the SU transmitter can transmit as long as inter-
ference caused to the PU receiver is below a threshold.
The channel gains from the SU transmitter to the SU
receiver and the PU receiver are denoted g1 and g0,
respectively. We denote the instantaneous transmit
power at the SU transmitter as P(g0, g1). In such a sys-
tem, the most common constraints to protect the PUs
are peak or average interference powers constraints
(IPCs). Under peak IPCs, the overall instantaneous
interference power generated by the SUs must be below
a threshold, Qpk, that is
g0P(g0,g1) Qpk, (g0,g1). (7)
Similarly, the constraint on the average interference
power can be expressed as
E[g0P(g0,g1)] Qav, (8)
where Qav is a threshold. Moreover, the transmit
power constraints (TPCs) of the SUs should be taken
into account. The peak TPC can be expressed as
P(g0,g1) Ppk, (9)
where Ppk is the peak transmit power limit. The aver-
age TPC can be expressed as
E[P(g0,g1)] Pav, (10)
where Pav is the average transmit power limit.
The power control for systems with single PU pair
and single SU pair have been investigated in [77-80]. In
[77], different kinds of capacity for the SU system, such
as the ergodic, outage, and minimum-rate, are deter-
mined for Rayleigh fading environments under both
peak and average IPCs. The analysis has been extended
to the case with TPCs in [78]. It is shown that the aver-age IPCs can provide higher capacities than the peak
average IPCs. If the statistics of any sensing metric con-
ditioned on the PU being ON/OFF are known a priori
to the SU transmitter, optimal power control schemes
[79] and adaptive rate and power control schemes [80]
have been proposed to maximize SU system capacity
subject to average IPCs and peak TPCs. The system
throughput can be improved using soft information.
More general models with multiple PUs and SUs have
been studied in [81]. The power allocation problems for
sum-rate maximization on Gaussian cognitive MAC
under mutual interferences between the PU and the SU
communications are formulated as a standard non-con-vex qu ad rati call y co nstrai ned quad ra tic pr oble m
(QCQP) where semidefinite relaxation (SDR) has been
applied to find a simple solution.
All the above study focuses on performance analysis
for SU systems while the performance of PU systems
under average and peak IPCs has been studied in [82].
It has been shown that the average IPCs can be advanta-
geous over the peak IPCs in most cases. Moreover, the
existing results demonstrated that the SU system can
get better performance under average IPCs. Thus, aver-
age IPCs should be used in practice to protect both PU
and SU systems.Besides using IPCs, PU outage probability constraint
(OPC) can be used to protect PU transmission [83,84],
where the outage probability of the PU transmission
should not be below a given threshold. Under the OPC
and average/peak TPCs, optimal power allocation strate-
gies have been developed to maximize the ergodic and
outage capacities of SU systems in [83]. It has better
performance than IPCs. By utilizing the outage informa-
tion from the PU receiver on the PU feedback channel
as an inference signal for coordination, a discounted dis-
tributed power control algorithm has been proposed in
PU
transmitter
PUreceiver
SU
transmitterSU
receiver
0g
1g
Figure 4 Spectrum sharing model.
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[84] to maximize the utilities of the SUs under OPC and
peak TPCs.
4.1.2. Multi-carrier and multi-channel systems
In a multi-carrier or multi-channel system, interference
generated by the SU to the PU can be considered either
in the whole bands/channels or each sub-band (sub-
channel) separately. Similar to the case of single-carrier
and single-antenna systems, the IPCs for the PUs can be
divided into two types: peak and average IPCs.
Power control schemes under different constraints for
both PU and SU systems have been extensively studied.
In [85], capacity maximization for the SU system under
TPCs as well as either peak or average IPCs is investi-
gated. It is shown that the average IPC provides better
performance for the SU system than the peak IPC.
Instead of using IPC directly, optimal power allocation
under OPC has been investigated [86]. With the CSIs of
the PU link, the SU link, and the SU-to-PU link at theSU, a rate loss constraint (RLC) has been proposed,
where the rate loss of the PUs due to the SU transmis-
sion should be below a threshold. Under RLC, the trans-
mission efficiency of the SU system increases [87]. From
practical point of view, a hybrid scheme by using both
IPC and RLC is analyzed as well. Since the spectrum
sensing results are not reliable, the probabilistic infor-
mation of channel availability has been used to assist
resource allocation in a multi-channel environment [88].
Compared with the conventional hard decision based
IPCs, the proposed approach can utilize the spectrum
more efficiently while protect the PUs from unaccepta-
ble interference. By considering the SINR requirements
for the SUs, downlink channel assignment and power
control schemes have been studied under the IPCs to
the PUs [89] to maximize the number of active SUs.
Besides the above co-existence scenario, another sce-
nario is in the multi-band system where PU and SU are
co-located in the same area with side-by-side bands. For
this scenario, power allocation schemes have been pro-
posed in [90]. A risk-return model, which includes these
two co-existence scenarios together, has been intro-
duced in [91], and takes into account the reliability of
the available sub-bands, their power constraints, and
IPCs to the PUs. Besides the optimal power allocation,three suboptimal schemes, namely, the step-ladder, nul-
ling, and scaling schemes have been developed.
4.1.3. Multi-antenna systems
For multi-antenna systems, most study jointly optimizes
power allocation and beamforming [92-95]. Under IPCs
and peak TPCs, the power allocation and beamforming
design for sum-rate maximization and signal-to-interfer-
ence-plus-noise ratios (SINRs) balancing problems have
been studied for SIMO systems in [92]. For SINR balan-
cing, all the SUs can achieve their targeted SINRs fairly.
When linear minimum mean-square-error (MMSE)
receivers are utilized, multiple constraints can be
decoupled into several subproblems with a single con-
straint. The study of SINR-balancing has been extended
into MIMO systems in [93], where a robust beamform-
ing design is developed to limit the interference leakage
to PU below a specific threshold with a certain probabil-
ity. Beamforming for MIMO systems has been proposed
to maximize the SINR of SUs under IPCs in [94]. A uni-
fied homogeneous quadratically constrained quadratic
program is used to solve the optimization problems. In
practice, it may be impossible that the SINR require-
ments of all the SUs are satisfied. For this situation, a
joint beamforming and admissi on control scheme has
been proposed to minimize the total transmit power of
the SU system under IPCs [95].
4.1.4. Multi-hop systems
In a relay-assisted system, interference from all relay
nodes to the PU receiver should be considered. In [ 96],transmit power allocation (TPA) schemes among relays
have been studied, where overall transmit power is
minimized under IPCs as well as the SINR requirement
of the targeting SU receiver. A fully distributed TPA has
been proposed and provides an almost optimal solution
as centralized solution. To reduce the energy consump-
tion of the fully distributed TPA, a distributed feedback
assisted TPA has been proposed by feeding back the
estimated real-valued transmit power. Besides power
allocation, channel allocation has been studied in [97].
In a three-node CR network, end-to-end transmission is
categorized into three modes, namely direct, dual-hop,
and relay channels. The optimal end-to-end channel and
power allocation scheme can be realized by analyzing
optimal power allocation for each mode and choosing
the mode that can provide the highest system through-
put. Joint rate, power control, and channel allocation
problem to maximize the throughput of the relay-
assisted SU system has been studied in [98], where an
optimal solution can be obtained by dividing the original
problem into a base station (BS) master problem and
the relay station subproblems.
4.2. Spectrum sharing game
Game theory is a well-developed mathematical tool tomodel strategic situations, where any individuals success
in making choices depends on the choices of others. In
the CR networks, the SUs compete for spectrum
resources to maximize their own utilities where game
theory models can be used [99]. Moreover, the game
theory can be also utilized to analyze both the PU and
SU systems behaviors, where they try to maximize their
own profits.
From the game-theoretical perspective, power control
can be formulated as a distributed game among all the
SUs [100], where SUs maximize their own utilities by
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adjusting transmission powers. The total CR network
capacity is maximized when the game reaches a Nash
equilibrium (NE). In a uplink multiuser CR system, a
non-cooperative power control game among SUs can be
applied to maximize energy efficiency with a fairness
constraint [101]. To jointly design waveform and
resource allocation, a general framework has been devel-
oped [102], which can be applied to many radio plat-
forms, such as frequency, time, or code division
multiplexing, and even agile framework with different
expansion functions.
The above study considers SU system only. To take
the activities of PUs into account, a dynamic spectrum
leasing paradigm based on a game-theoretical frame-
work has been proposed [103,104], which provides an
i nc en ti ve f or t he P Us t o a ct iv el y a ll ow S Us
transmission.
Besides for the resource allocation, game theory canalso have other applications in CR networks, such as sti-
mulating the cooperation between SUs [105] and com-
bating PU emulation attacks [106,107].
However, there are still unaddressed issues in this
area. Only spectrum sharing constraints are imposed on
the games in most of existing study. Deviations from
the ideal games due to non-cooperating SUs or channel
constraints still need further study.
4.3. Spectrum decision
If there are multiple available bands, the CR networks
should be able to decide the best one for each SU. This
procedure is called spectrum decision [108,109]. Com-
pared to traditional spectrum sharing that is designed as
a short-term operation to adapt to the fast time-varying
channels, spectrum decision considers long-term chan-
nel characteristics. Generally, spectrum decision consists
of two main steps: channel characterizing and spectrum
assigning. Channel characteristics such as path loss, link
errors, and link delay are first obtained based on both
current observations of SUs and statistics information of
PUs. The best spectrum band is then assigned to each
SU. In [109 ], a spectrum decision framework for CR
networks has been discussed. A minimum variance-
based and a maximum capacity-based spectrum decisionschemes have been developed for real-time applications
and best-effort applications, respectively. Moreover,
learning mechanisms can be equipped to assist the deci-
sion making procedure [110-112]. In [110], the use of
reinforcement learning algorithm for spectrum assign-
ment in WCDMA systems has been evaluated to show
the satisfactory results under different load conditions.
Based on the learning mechanisms, the spectrum deci-
sion making problem can also be modeled as a multi-
armed bandit problem, which can be solved by upper
confidence bound algorithm [111 ,112]. Even though
there exists some study about spectrum decision, it is
still an open topic.
4.4. Spectrum handoff
During the transmission of SUs, the PUs may appear to
claim their assigned channels. When this occurs, SUs
need to stop transmission, vacate the channels in con-
tention, and find other available channels to continue
transmission, which is called spectrum handoff [6].
When a SU changes its spectrum, its transmission sus-
pends, which invariably leads to latency increase. To
ensure smooth and fast transition with minimum per-
formance degradation, a good spectrum handoff
mechanism is required [113-117]. One way to alleviate
latency is to reserve a certain number of spectrum
bands for spectrum handoff [113]. SUs immediately use
the reserved spectrum bands when it is necessary. How-
ever, the number of the reserved bands should be cho-sen carefully to balance the spectrum efficiency and
handoff performance. Another way is proactive spec-
trum handoff [114], where SUs vacate channels before
PUs utilize them to avoid collision. This is obviously too
idealistic in many settings unless the SU is capable of
predicting PU behavior as in opportunistic spectrum
access. Its performance gain over the reactive spectrum
handoff has been demonstrated. To evaluate the latency
performance of spectrum handoff, an analytical frame-
work has been proposed in [115]. The effects of general
service time distribution, various operating channels,
and queueing behaviors of multiple SU connections are
also considered.
To achieve reliable continuous transmission, SUs can
choose spectrum bands owned by different PUs
[116,117]. Even if a PU reappears, SUs can continue
transmission on others. The spectrum diversity also
helps to increase the transmission reliability.
4.5. Transmission design for CR systems
In CR networks, the transmission protocols of the SUs
can be designed to achieve satisfactory system perfor-
mance together with fulfilling the IPCs for the PUs.
Relays are the most common choices for increasing
SU system performance, as shown in Figure 5. Unlikethe traditional cooperation transmission system, the per-
formance of the cooperative SU system decreases by
introducing the IPCs. For example, the outage probabil-
ity may increase [118]. With the increase of the number
of relays, the outage performance gap decreases. The
relays in the CR networks can assist the transmission
between the SU transmitter and receiver to reduce the
required transmit power and decrease the interference
generated to the PU system [119]. The transmission
opportunities created through this way are called as spa-
tial SHs. The relay selection for outage performance
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optimization of the SU system under either IPCs [120]
or OPC [121] for the PU system have been studied.
Power allocation and relay selection can be designed
jointly to maximize SU system throughput [122]. More-
over, spectrum sensing and relay selection can be
designed jointly [74,123]. Without a dedicated relaychannel, the best relay is selected for transmission based
on the sensing information from the relays that detect
the PU signals.
The SU itself can work as a relay to assist PU trans-
mission while transmitting its own data at the same
time [124,125]. A two-phase transmission protocol in a
single antenna system has been considered in [124],
where the PU transmits in the first phase and the SU
transmitter assigns fractions of the available power to
the PU data and its own data for the second phase
transmission, respectively. This protocol guarantees the
performance of the PU system. With the help of a
multi-antenna SU transmitter, both PU and SU systems
can achieve better performance by beamforming at the
AF SU transmitter [125].
To reduce interference in a MIMO system, the spatial
freedom created by antennas can be used. An opportu-
nistic spatial orthogonalization (OSO) scheme [126] has
been proposed. With OSO, the SU system can get sig-
nificant performance improvement without sacrificing of
PU systems performance much. Taking the time dimen-
sion into account, the interference-free co-existing PU
and SU transmission can be achieved based on orthogo-
nal space-time coding [15].
4.6. Challenges
4.6.1. Joint sensing and access
In most existing work, the sensing and access techni-
ques are designed separately. However, these two parts
are interactive. With different objectives of SU systems,
different protection levels for PU systems are needed. It
is beneficial to consider these two parts jointly [127]. A
joint sensi ng time and power al location scheme has
been studied in [127] under the average IPCs and TPCs
to maximize the ergodic throughput of SU systems.
More study is desired in this topic.
4.6.2. Channel estimation
The link condition between PU and SU is an important
parameter for most of techniques in CR networks. How-
ever, the SU-to-PU channel gain is difficult to be
obtained by the SU in practice. To estimate the link
condition, the SU can observe PUs behavior by sendingout a disturbing probing signal [128] or eavesdrop PUs
automatic repeat request feedback information [129]. To
avoid estimation of the actual channels between SU
transmitter and PUs, a novel practical cognitive beam-
forming [130] has been proposed based on the effective
interference channel, which combines information of
the actual channels and the beamforming vectors used
by the PUs.
5. Applications of CRThe development of spectrum sensing and spectrum
sharing techniques enable the applications of CR inmany areas. In this section, we introduce some of them.
5.1. TV white spaces
The main regulatory agencies for the unlicensed use of
TV white spaces are the FCC in the United States, the
Office of Communications (Ofcom) in the United
Kingdom, and the Electronic Communications Com-
mittee (ECC) of the conference of European Post and
Telecommunications in Europe. After many years of
effort in this area, FCC released the final rules for
using the TV white space in September 2010 [131],
which led to the culmination of this field. Meanwhile,
other agencies have also been getting progress [132 ].
This is based on the idea of having an accessible data-
base (centralize-fashion) of free TV bands, otherwise
called TV white space, or to sense and obtain SHs
(distributed-fashion) within TV bands to utilize for
SUs communication.
5.2. Cellular networks
The applications of CR in cellular networks are emer-
ging in recent years. To overcome the indoor coverage
problem and adapt to traffic growth, the concept of
small cells, such as femtocells, has been proposed in
3GPP LTE-Advanced (LTE-A) [133] and IEEE 802.16m[134], and companies like PicoChip driving femtocell
revolution. The femtocell unit has the function of the
typical BS (eNodeB in LTE). However, the self-deploy-
ment property of the femtocells makes the centralized
interference management impractical. Figure 6 illus-
trates the femtocell interference scenario, where dis-
tributed spectrum planning schemes are needed. With
CR, the femtocells can search and estimate the avail-
able spectrum bands in order to maintain the coverage
and avoid the interference to other femtocells and
macrocells.
PU
transmitterSU
transmitterSU
receiver
SU1 SUi SUk
... ...
PU
receiver
Figure 5 Relay-aided transmission in SU system.
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5.3. Military usage
CR is a must-have technique for military usage. With
CR, the users can recognize the enemies communica-
tions and protect their owns. Moreover, the users can
search for more transmission opportunities. The USdepartment of defense (DoD) has already established
programs such as SPEAKeasy radio system and next
Generation (XG) to exploit the benefits of CR techni-
ques [6].
5.4. Emergency networks
Under severe conditions or extreme situations, e.g., nat-
ural disasters and accidents, first re-sponders need to
detect and locate survivors and maintain reliable com-
munications between responders and public safety agen-
cies. The infrastructure of the current wireless
communication systems is inadequate to meet the futuredemands of emergency response. The opportunistic
spectrum usage provided by CR techniques could be
used to realize efficient and reliable emergency network
transmission. The FCC has designated a 700 MHz (698-
806 MHz) frequency band for emergency use [135,136].
The public safety broadband licensee will have priority
to access these portions of the commercial spectrum
during emergency. Still, there remain some challenges
for emergency networks. Possible applications for CR
include accurately locating survivors in extreme environ-
ments and improving reliability and efficiency of com-
munications. Energy efficiency is particularly important
because battery life limits successful operations.
6. ConclusionsCR technology has been studied to increase the spec-
trum utilization efficiency. With spectrum sensing tech-
niques, the SUs are able to monitor the activities of the
PUs. To address the limitations of the spectrum sensing
techniques by a single SU, CSS schemes have been dis-
cussed. Based on the spectrum sensing results, the SUs
can access the spectrum bands under the interference
limit to the PUs. Different spectrum sharing and
allocation schemes have been considered to increase the
spectrum efficiency. Even though many critical issues in
CR have been addressed in the past decade, there are
still some challenges. Nevertheless, we believe the CR
technology will be applied to many real systems in the
near future.
Due to the space limitation, we have left out some
topics, such as security, policy issues, and CR implemen-
tation architecture. The readers can refer to some other
publications in recent special issues, e.g., [137-140],
which may cover these topics.
Acknowledgements
This study was supported in part by the research project from Sandia
National Laboratories.
Competing interests
The authors declare that they have no competing interests.
Received: 1 May 2011 Accepted: 31 January 2012
Published: 31 January 2012
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Figure 6 Illustration of femtocell interference.
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